Monitoring Of Behavior Research Articles

Multifunctional Ta4C3Tx MXene/HTPP - PBS composites with multi cross-linking systems to cope with complex nuclear environments

The development of multifunctional radiation protection materials is crucial to ensure the safety of personnel and equipment in complex nuclear environments. Herein, a flexible radiation protection material integrating self-healing, thermal management, and behaviour response was prepared from Ta4C3Tx MXene, Hydrogen-terminated phenyl polysiloxane, and Polyborosiloxane through esterification, “thiol-ene click” hydrosilylation reaction using L(+)-Cysteine as a bridge. The results showed that when Ta4C3Tx MXene reached 20 wt%, the tensile strength of composite was 0.6321 MPa with the self-healing rate at 90.22 %. And the gauge factor of the material reached 25.83 at 100 % strain. This compensated for the gap between low strain and high sensitivity. Additionally, the multilayer structure of Ta4C3Tx MXene and the absorption properties of Tantalum enable the material to attenuate γ-rays through absorption and scattering mechanisms. The attenuation capability of Ta2O5 and Ta4C3Tx MXene fillers for 59.6 KeV γ-rays was compared at the same Tantalum content. When the Tantalum content reaches 23.81 wt%, the Ta4C3Tx MXene composite contains 20 wt% filler and has an attenuation coefficient of 0.757. Similarly, the Ta2O5 composite contains 22.5 wt% filler and has an attenuation coefficient of 0.734. These results are significant for improving temperature and behaviour monitoring of workers in low-temperature nuclear environments.

Revenue forecasting in smart retail based on customer clustering analysis

Understanding your customers is among one of the most important strategies to boost retail profit. In this research, we propose a WiFi-based sensing method to analyze customer behaviors. The monitoring of customer behaviors may lead to revenue growth. Specifically, the strategy is focused on understanding and grouping customers’ behaviors in which we track customers who share similar visiting patterns through WiFi sensing. Accordingly, we can have group-based prediction done for customers who own similar behaviors. We extract customers’ visiting patterns including the customers’ Service Set Identifier list and related information. After all, the proposed system is realized in a cafeteria place where we have the deployed WiFi access points continuously collect data over a time horizon of three months to serve as the inputs for data analysis. The data samples include the number of customers’ devices, number of products and revenue amounts. The dataset also integrates group information and weather conditions. We adopt several machine learning methods including Support Vector Regression and Random Forest for model induction. We conduct these models in terms of three main prediction tasks consisting of coffee shop’s revenue, the number of products, and the number of customers’ devices for evaluation. Furthermore, considering these predictions, we separate between the staying-in and to-go parts. Based on the experiment result, customers’ group information helps, as well as weather conditions. Overall, we can achieve the best prediction result when both the group information and weather conditions are included where we can enjoy as good as 6% to 10% in MAPE.

Emergency repairs downstream of earth and rockfill dams with water spring with indication of internal erosion

During periodic dam safety assessments, various anomalies are identified through field inspections, project documentation analysis, and structural behavior monitoring. These assessments aim to detect non-conformities, and engineering design firms propose technical solutions to address these issues. The primary goal of these reviews is to evaluate the overall state of conservation and structural safety, ensuring compliance with updated design criteria, revised standards, changing weather conditions, and technological advances. Dam renovations are crucial for enhancing operational capacity and preventive care. Implementing filtering materials within the dam's body establishes a drainage system to relieve internal pressures on the embankments and foundations. This prevents the washing away of soil particles due to internal erosion, known as "piping," a major cause of dam failures. Piping occurs when soil particles are displaced towards the outside of the embankment through the percolation process, particularly in the upstream area near the dam's base, where high hydraulic gradients and flows make it susceptible. This article presents a set of emergency or long-term solutions to address anomalies related to the onset of internal erosion in dams, with a focus on preventing piping. These solutions are vital for safeguarding the structural integrity and safety of these critical infrastructure components.

Privacy versus pedagogy – students’ perceptions of using learning analytics in higher education

The rapid recent use of learning analytics (LA) in higher education, specifically during the COVID-19 pandemic, allows the monitoring of users' behavior while learning. Using LA may promote students' learning outcomes but also intrude into their privacy. This study aimed to explore students’ behaviour and perceptions towards privacy and data protection when using LA for pedagogical needs, examine the privacy trade-off of students' willingness to share personal information in exchange for pedagogical benefits and understand the predicting variables for this privacy trade-off. A model was developed containing five groups of influencing variables (demographic data, perceptions, feelings, behaviour and awareness) on the privacy trade-off. A total of 1,014 students completed an online questionnaire. The results found that students do care about their privacy but are not aware of privacy and data protection regulations. They are willing to trade off privacy for pedagogical benefits, and they trust their academic institutions, but they want transparency. Age, a sense of security in the academic institution, behaviour, data misuse concern and institution management of students' personal information are the significant predictors for a privacy trade-off. It is important to engage students in the process as they are the main beneficiaries of LA and build trust between them and the institution. Implications for practice or policy: Academic institution should actively provide information to raise privacy awareness. Academic institutions should engage students in the process of using LA to create a high degree of trust. Universities should endorse LA policy, fostering it for pedagogical purposes. Instructors may utilise LA to enrich student learning, respecting their willingness to share pedagogical data. Academic institutions should provide a high level of transparency in order to build students’ trust in their institutions.

Circadian Rhythm and Sleep Analyses in a Fruit Fly Model of Fragile X Syndrome Using a Video-Based Automated Behavioral Research System.

Fragile X syndrome (FXS) is caused by the full mutation in the FMR1 gene on the Xq27.3 chromosome region. It is the most common monogenic cause of autism spectrum disorder (ASD) and inherited intellectual disability (ID). Besides ASD and ID and other symptoms, individuals with FXS may exhibit sleep problems and impairment of circadian rhythm (CR). The Drosophila melanogaster models of FXS, such as dFMR1B55, represent excellent models for research in the FXS field. During this study, sleep patterns and CR in dFMR1B55 mutants were analyzed, using a new platform based on continuous high-resolution videography integrated with a highly-customized version of an open-source software. This methodology provides more sensitive results, which could be crucial for all further research in this model of fruit flies. The study revealed that dFMR1B55 male mutants sleep more and can be considered weak rhythmic flies rather than totally arrhythmic and present a good alternative animal model of genetic disorder, which includes impairment of CR and sleep behavior. The combination of affordable videography and software used in the current study is a significant improvement over previous methods and will enable broader adaptation of such high-resolution behavior monitoring methods.

Machine learning-based understanding of aquatic animal behaviour in high-turbidity waters

Inspired by the ambitions envisioned in the Fourth Industrial Revolution for aquaculture, also known as Aquaculture 4.0, the aquaculture (marine animal farming) industry is seeking to adopt data-driven Artificial Intelligence (AI) to help significantly improve business operations. One of the major barriers is the manual annotation of animal behaviour data, which is a time-consuming task that demands high levels of concentration from biologists. To address this challenge, this paper proposes novel automatic animal behaviour monitoring tailored for industrial scenarios. Our approach introduces a real-time machine-learning-based instance segmentation system that is specialised for underwater environments, where large groups of shrimp are farmed. The implemented system achieves an accuracy rate of 89% at 30 frames per second (fps) and can accurately detect shrimp in high-density areas under poor lighting conditions and high turbidity waters, despite the challenges of occlusion and overlapping. A key innovation of our method is the implementation of a new density cluster algorithm for time series and video analysis. This approach provides a more efficient and accurate way of monitoring animal behaviour, significantly saving time and effort for biologists and advancing the capabilities of automated aquaculture systems.

RpTrack: Robust Pig Tracking with Irregular Movement Processing and Behavioral Statistics

Pig behavioral analysis based on multi-object tracking (MOT) technology of surveillance videos is vital for precision livestock farming. To address the challenges posed by uneven lighting scenes and irregular pig movements in the MOT task, we proposed a pig MOT method named RpTrack. Firstly, RpTrack addresses the issue of lost tracking caused by irregular pig movements by using an appropriate Kalman Filter and improved trajectory management. Then, RpTrack utilizes BIoU for the second matching strategy to alleviate the influence of missed detections on the tracking performance. Finally, the method utilizes post-processing on the tracking results to generate behavioral statistics and activity trajectories for each pig. The experimental results under conditions of uneven lighting and irregular pig movements show that RpTrack significantly outperforms four other state-of-the-art MOT methods, including SORT, OC-SORT, ByteTrack, and Bot-SORT, on both public and private datasets. The experimental results demonstrate that RpTrack not only has the best tracking performance but also has high-speed processing capabilities. In conclusion, RpTrack effectively addresses the challenges of uneven scene lighting and irregular pig movements, enabling accurate pig tracking and monitoring of different behaviors, such as eating, standing, and lying. This research supports the advancement and application of intelligent pig farming.

Vehicle Monitoring and Tracking System

In this paper we present a vehicle monitoring and tracking system. Previous systems focus solely on either theft detection or car/driver monitoring. In this paper we provide an integrated solution that provide both theft detection, tracking and driver behavior monitoring. Our system performs well in detecting theft attempts through RFID and tracking location by GPS. It also monitors the driver behavior using OBD reader. Combining both features in one system has great advantages like cost efficiency, space saving and the possibility of adding new features.

Research on Load Decomposition and Optimization of Intelligent Elderly Care Service Based on Heuristic and Event Detection Algorithm

Against the backdrop of the digital age, the openness, equality and interaction of the Internet economy have injected new vitality into China’s traditional industries. The application of big data technology, especially in information integration and analysis, has become a key force in promoting the sustainable and healthy development of the national economy. This study focuses on the “Internet +” environment, discusses the impact of the aging problem of community workers on home care services, and proposes an optimization scheme based on a heuristic algorithm. The heuristic algorithm, inspired by the foraging behavior of ants in nature, optimizes the route selection problem by simulating an ant colony to choose the path with a high concentration of pheromones and shows outstanding application potential in the field of home care. The accuracy of the event detection algorithm is directly related to the performance of the load decomposition algorithm, and the change point detection algorithm can effectively identify the change point of the probability distribution in the time series data, which provides important input data for unsupervised clustering. Advanced computer theory, including the Hidden Markov model (HMM) and swarm intelligence optimization algorithm, is used in this research. By comparing different swarm intelligence algorithms, we find that the standard Gray Wolf optimization (SGWO) model is better than the basic Gray Wolf optimization (BGWO) algorithm and the improved Gray Wolf optimization (DGWO) algorithm in terms of stability and output results. The SGWO model significantly improves the efficiency of the load decomposition algorithm, which has been verified in the application of the smart elderly care service platform. The platform not only supports the operation of related technologies and information products but also realizes the seamless integration of information among various subjects of elderly care services. In addition, the factor hidden in the Markov model that can be selectively activated effectively monitors equipment status in the Internet of Things environment, provides real-time monitoring of user consumption behavior and fault information and further enhances the quality and efficiency of smart elderly care services.

Abnormal behavior monitoring enhanced smart university stadium under the background of “Internet plus”

AbstractWith the rapid development of the Internet of Things and 5G technology, smart university gymnasiums have become more and more important. However, it has become increasingly difficult for university gymnasium management, especially to detect abnormal behavior with dense crowds under limited venue space. To handle this issue, this paper designs an Artificial Intelligence Internet of Things (AIoT) abnormal behavior detection system which consists of the 5G camera, 5G transmission network and cloud platform. The 5G camera captures and transmits the video to the cloud platform by exploiting the 5G wireless sensor network. In the cloud platform, a hybrid variational autoencoder backbone which exploits the pre‐trained VGG16 and Transformer model is deployed to detect abnormal behaviors. Moreover, by introducing adversarial training mechanisms, the robustness of the proposed model is effectively improved. The experimental results on our self‐built gymnasium abnormal behavior dataset show that the proposed model can correctly identify most of the abnormal behaviors in the gymnasium compared to other models.

Soundscape characteristics of RAS tanks holding Atlantic salmon (Salmo salar) during feeding and feed withdrawal

Behavioural monitoring can provide crucial information on welfare and feeding in aquaculture. Passive acoustic monitoring of behaviour can be particularly useful in recirculating aquaculture systems (RAS), as they often have turbid water that impairs visual monitoring. Currently, little is known about the sounds that make up the soundscapes in RAS tanks holding Atlantic salmon (Salmo salar). In this study, hydrophones were used to continuously record the soundscape in eight single tank RAS holding Atlantic salmon parr for 15 days, with the fish in four of the tanks being fasted by feed withdrawal for five days from the sixth to the tenth day. The results show that soundscapes in RAS tanks are affected by feeding. Two main sound sources were identified during feeding in RAS tanks, one related to pellets delivery and the other to fish behaviour. The sound of pellets hitting the water surface had energy concentrated at frequencies between 1.7 and 4.0 kHz, with peak frequency decreasing and amplitude increasing with increasing number of pellets hitting simultaneously. The feeding sounds of Atlantic salmon had energy concentrated at frequencies between 6.5 and 9.4 kHz.More complex soundscapes were recorded during feeding events. These were characterized by variations in amplitude and frequency that have been described by using acoustic indexes in RAS tanks for the very first time. The Acoustic Complexity Index (ACI), the Acoustic Entropy Index (H) and the Normalized Difference Soundscape Index (NDSI) showed distinct changes in the soundscape related to feeding events; ACI increased while H and NDSI decreased compared to the times in between scheduled feeding times. The sound types identified in this study and the outcomes of the acoustic indices indicate a possibility to monitor system performance as well as fish behaviour in the tank soundscapes in RAS. Soundscape monitoring can contribute to match feeding closer to fish appetite, improve water quality, and reduce risks that deviations in the system performance can have on fish welfare during production.

Exploring Behaviour Change of Healthcare Professionals while Implementing a Complex Nursing Intervention: A Survey Study

In this study we explore the change in behaviour in terms of capability, opportunity and motivation of all healthcare professionals involved in the implementation of the complex nursing intervention PREDOCS in twelve cardiac surgery centres in the Netherlands. A multicentre survey in twelve Dutch cardiac surgery centres, implementing a complex nursing intervention: the PREDOCS consultation. Convenience samples of involved healthcare professionals completed the questionnaire, in 2018 (n = 92) and 2019 (n = 73). The COM-B-QP questionnaire based on COM-B items, MIDI items and open fields was developed and adapted to context-specific factors of every participating hospital. Depending on their role, participants rated their level of agreement with 14–26 predefined statements. The median and interquartile range and the difference/delta in medians at two-time points were calculated. At both moments in time, high scores were identified with no significant difference in five COM-B items: psychological capability, physical and social opportunity and reflective and automatic motivation. Professionals were generally well informed about the PREDOCS consultation and the PREDOCS consultation fits within their current work and profession. Staff shortages, staff turnover and insufficient insight into the effect were reported as reasons that hindered further implementation. We measured no significant change in the behaviour of healthcare professionals over time. It is unclear to what extent staff shortages, staff turnover and insufficient insight into effect influenced behaviour. Given the complexity of implementation, repeated monitoring of behaviour seems useful, so that continuous adjustment of the implementation strategy can take place.

The ReCoDe addiction research consortium: Losing and regaining control over drug intake-Findings and future perspectives.

Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.

Dynamic behavior monitoring of twin supertall buildings during Super Typhoon Soksuri using social sensing data

This paper presents the dynamic behavior monitoring of the 300-m-tall Xiamen twin towers during Super Typhoon Soksuri by means of social sensing, which involves two individuals equipped with six smart devices to conduct acceleration measurement and video footage filming. The measured acceleration data are first corrected by the resampling technique since the sampling rate of smart-device-based measurement is unstable, and the corrected building acceleration obtained from different smart devices shows good consistency. Based on the corrected building acceleration responses, the amplitude-dependent and time-variant structural dynamic properties of the monitored twin towers are revealed by the random decrement technique and the Bayesian spectral density approach, respectively. The determined structural dynamic properties are further compared with seven existing prediction models to evaluate their accuracy. Moreover, by employing a computer-vision technique (i.e., the Kanade-Lucas-Tomasi optical-flow approach), the structural dynamic displacement was extracted from the video footage filmed during Soksuri, and its accuracy was further validated through comparison with direct acceleration measurements. This paper aims to demonstrate the feasibility and effectiveness of social sensing for dynamic behavior monitoring of supertall buildings.

Revisiting vision-based violence detection in videos: A critical analysis

An ever-increasing installation of surveillance cameras at different places for ensuring public safety, security and asset protection has triggered the need for intelligent video surveillance to monitor the people and their behavior. Violence detection is a prominent application of intelligent surveillance as it plays a vital role in public safety, behavior monitoring, and law enforcement. It deals with identifying whether the violent event or behavior occurred in video sequence or not. Various researchers have developed different techniques and features for the detection of violence in recent years. The purpose of this paper is to provide an expository study of various state-of-the-art approaches for detecting violence in videos. It is evident from the ongoing effort in this field and with the advancement in computer vision technology, previous approaches get surpassed and new methods and features always keep on developing. Therefore, in order to complement ongoing research, it is also imperative to conduct a comprehensive analysis of different works from time to time. In this survey, each work has been critically analyzed, along with its pros and cons. The violence detection techniques have been divided under three categories: handcrafted features based, deep learning and hybrid violence detection approaches that have been extensively reviewed in various sub-categories. The major challenges faced by researchers and the steps involved in the process of violence detection are also discussed. Moreover, unlike typical practices of comparing the performance of models within same datasets, we examined a few models for cross-dataset assessment and revealed their limited generalization. This emphasizes the crucial need for robust model generalization to ensure efficacy across varied real-world scenarios. Finally, the paper carried out a discussion of various research gaps in the current approaches and the possible solutions to be taken to resolve them, laying a solid foundation for future work in this area.

Atomic Force Microscopy Lifetime Analysis: An Intuitive Method for Evaluating Receptor Tyrosine Kinase Dimer-Targeting Inhibitors.

Overexpression of receptor tyrosine kinases (RTKs) or binding to ligands can lead to the formation of specific unliganded and liganded RTK dimers, and these two RTK dimers are potential targets for preventing tumor metastasis. Traditional RTK dimer inhibitor analysis was mostly based on end point assays, which required cumbersome cell handling and behavior monitoring. There are still challenges in developing intuitive process-based analytical methods to study RTK dimer inhibitors, especially those used to visually distinguish between unliganded and liganded RTK dimer inhibitors. Herein, taking the mesenchymal-epithelial transition factor (MET) receptor, an intuitive method for evaluating MET inhibitors has been developed based on atomic force microscopy (AFM) lifetime analysis. The time interval between the start of the force and the bond break point was regarded as the bond lifetime, which could reflect the stability of the MET dimer. The results showed that there was a significant difference in the lifetime (τ) of unliganded MET dimers (τ1 = 207.87 ± 4.69 ms) and liganded MET dimers (τ2 = 330.58 ± 15.60 ms) induced by the hepatocyte growth factor, and aptamer SL1 could decrease τ1 and τ2, suggesting that SL1 could inhibit both unliganded and liganded MET dimers. However, heparin only decreased τ2, suggesting that it could inhibit only the liganded MET dimer. AFM-based lifetime analysis methods could monitor RTK dimer status rather than provide overall average results, allowing for intuitive process-based analysis and evaluation of RTK dimers and related inhibitors at the single-molecule level. This study provides a novel complementary strategy for simple and intuitive RTK inhibitor research.

Development of flexible multi-phase barium titanate piezoelectric sensor for physiological health and action behavior monitoring

Self-powered wearable piezoelectric sensing devices demand flexibility and high voltage electrical properties to meet personalized health and safety management needs. Aiming at the characteristics of piezoceramics with high piezoelectricity and low flexibility, this study designs a high-performance piezoelectric sensor based on multi-phase barium titanate (BTO) flexible piezoceramic film, namely multi-phase BTO sensor. The substrate-less self-supported multi-phase BTO films had excellent flexibility and could be bent 180° at a thickness of 33 μm, and exhibited good bending fatigue resistance in 1 × 10 4 bending cycles at a thickness of 5 μm. The prepared multi-phase BTO sensor could maintain good piezoelectric stability after 1.2 × 10 4 piezoelectric cycle tests. Based on the flexibility, high piezoelectricity, wearability, portability and battery-free self-powered characteristics of this sensor, the developed smart mask could monitor the respiratory signals of different frequencies and amplitudes in real time. In addition, by mounting the sensor on the hand or shoulder, different gestures and arm movements could also be detected. In summary, the multi-phase BTO sensor developed in this paper is expected to develop convenient and efficient wearable sensing devices for physiological health and behavioral activity monitoring applications.

Ambient nano RF-Energy driven self-powered wearable multimodal real-time health monitoring

As the trend of population aging intensifies, the demand for continuous and efficient health monitoring for solitary elderly individuals and those with limited self-care capabilities is growing. Traditional wearable health monitoring devices primarily rely on battery power, which not only incurs high maintenance costs but also risks interruption of monitoring due to battery depletion. To address these issues, this paper introduces a self-powered flexible wearable monitoring device utilizing far-field Radio Frequency Energy Harvesting (RFEH) technology. This device powers integrated sensors and Bluetooth by harvesting RF energy from ambient Wi-Fi and other wireless signals, enabling real-time monitoring of the wearer's physical behaviour and health status with immediate feedback via mobile terminals. Under conditions of 100 cm distance and a power intensity of 0.8 dBm, the system can charge a 220 μF capacitor to 4.12 V within just 23.24 seconds, ensuring stable operation of the device. Moreover, the monitoring device is equipped with a low-power wireless sensor system capable of sampling up to 100 Hz, which accurately and promptly tracks and analyzes key health indicators such as walking, physiological activities, and respiratory status. This technology provides a reliable health monitoring solution for elderly individuals living alone and those with difficulties in self-care, significantly enhancing the effectiveness of remote medical services, improving their quality of life, and reducing the occurrence of emergency medical events. This research not only advances wearable device technology but also paves new paths for health management in an aging society.

Digital Twin—A Review of the Evolution from Concept to Technology and Its Analytical Perspectives on Applications in Various Fields

Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates the current state of DT technology but also anticipates its future trajectory and impact across multiple domains.

Acute nicotine vapor normalizes sensorimotor gating and reduces locomotor activity deficits in HIV-1 transgenic rats.

Despite improved life expectancy of people with HIV (PWH), HIV-associated neurocognitive impairment (NCI) persists, alongside deficits in sensorimotor gating and neuroinflammation. PWH exhibit high smoking rates, possibly due to neuroprotective, anti-inflammatory, and cognitive-enhancing effects of nicotine, suggesting potential self-medication. Here, we tested the effects of acute nicotine vapor exposure on translatable measures of sensorimotor gating and exploratory behavior in the HIV-1 transgenic (HIV-1Tg) rat model of HIV. Male and female HIV-1Tg and F344 control rats (n=57) were exposed to acute nicotine or vehicle vapor. Sensorimotor gating was assessed using prepulse inhibition (PPI) of the acoustic startle response, and exploratory behavior was evaluated using the behavioral pattern monitor (BPM). Vehicle-treated HIV-1Tg rats exhibited PPI deficits at low prepulse intensities compared to F344 controls, as seen previously. No PPI deficits were observed in nicotine-treated HIV1-Tg rats, however. HIV-1Tg rats were hypoactive in the BPM relative to controls, whilst nicotine vapor increased activity and exploratory behavior across genotypes. Cotinine analyses confirmed comparable levels of the primary metabolite of nicotine across genotypes. Previous findings of PPI deficits in HIV-1Tg rats were replicated and, importantly, attenuated by acute nicotine vapor. Evidence for similar cotinine levels suggest a nicotine-specific effect in HIV-1Tg rats. HIV-1Tg rats had reduced exploratory behavior compared to controls, attenuated by acute nicotine vapor. Therefore, acute nicotine may be beneficial for remediating sensorimotor and locomotor activity deficits in PWH. Future studies should determine the long-term effects of nicotine vapor on similar HIV/NCI-relevant behaviors.